End of training

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9774074074074074
+      value: 0.9850249584026622
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0734
-- Accuracy: 0.9774
+- Loss: 0.0340
+- Accuracy: 0.9850
 
 ## Model description
 
@@ -67,9 +67,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.2944        | 1.0   | 95   | 0.1433          | 0.9511   |
-| 0.2061        | 2.0   | 190  | 0.0835          | 0.9722   |
-| 0.1624        | 3.0   | 285  | 0.0734          | 0.9774   |
+| 0.1854        | 0.99  | 21   | 0.0688          | 0.9800   |
+| 0.0438        | 1.98  | 42   | 0.0410          | 0.9817   |
+| 0.0194        | 2.96  | 63   | 0.0340          | 0.9850   |
 
 
 ### Framework versions

all_results.json

@@ -0,0 +1,12 @@
+{
+    "epoch": 2.96,
+    "eval_accuracy": 0.9850249584026622,
+    "eval_loss": 0.03396075218915939,
+    "eval_runtime": 35.4524,
+    "eval_samples_per_second": 16.952,
+    "eval_steps_per_second": 0.282,
+    "train_loss": 0.1437178479418868,
+    "train_runtime": 1116.2364,
+    "train_samples_per_second": 14.524,
+    "train_steps_per_second": 0.056
+}

config.json

@@ -17,30 +17,14 @@
   "hidden_dropout_prob": 0.0,
   "hidden_size": 768,
   "id2label": {
-    "0": "AnnualCrop",
-    "1": "Forest",
-    "2": "HerbaceousVegetation",
-    "3": "Highway",
-    "4": "Industrial",
-    "5": "Pasture",
-    "6": "PermanentCrop",
-    "7": "Residential",
-    "8": "River",
-    "9": "SeaLake"
+    "0": "REAL",
+    "1": "SPOOF"
   },
   "image_size": 224,
   "initializer_range": 0.02,
   "label2id": {
-    "AnnualCrop": 0,
-    "Forest": 1,
-    "HerbaceousVegetation": 2,
-    "Highway": 3,
-    "Industrial": 4,
-    "Pasture": 5,
-    "PermanentCrop": 6,
-    "Residential": 7,
-    "River": 8,
-    "SeaLake": 9
+    "REAL": 0,
+    "SPOOF": 1
   },
   "layer_norm_eps": 1e-05,
   "mlp_ratio": 4.0,

eval_results.json

@@ -0,0 +1,8 @@
+{
+    "epoch": 2.96,
+    "eval_accuracy": 0.9850249584026622,
+    "eval_loss": 0.03396075218915939,
+    "eval_runtime": 35.4524,
+    "eval_samples_per_second": 16.952,
+    "eval_steps_per_second": 0.282
+}

pytorch_model.bin

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train_results.json

@@ -0,0 +1,7 @@
+{
+    "epoch": 2.96,
+    "train_loss": 0.1437178479418868,
+    "train_runtime": 1116.2364,
+    "train_samples_per_second": 14.524,
+    "train_steps_per_second": 0.056
+}

trainer_state.json

@@ -0,0 +1,91 @@
+{
+  "best_metric": 0.9850249584026622,
+  "best_model_checkpoint": "swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-63",
+  "epoch": 2.9647058823529413,
+  "eval_steps": 500,
+  "global_step": 63,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
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+      "learning_rate": 4.732142857142857e-05,
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+      "learning_rate": 3.839285714285715e-05,
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+      "eval_accuracy": 0.9800332778702163,
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+      "eval_steps_per_second": 0.283,
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+      "total_flos": 3.9908775915690394e+17,
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+      "train_samples_per_second": 14.524,
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+  "logging_steps": 10,
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+}

training_args.bin

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